Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

M.E. Borges* (Corresponding Author), L.S. Ferreira, S. Poloni, A.M. Bagattini, C. Franco, M.Q.M. da Rosa, L.M. Simon, S.A. Camey, R.D.S. Kuchenbecker, P.I. Prado, J.A.F. Diniz-Filho, R.A. Kraenkel, R.M. Coutinho, C.M. Toscano

*Corresponding author for this work

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Abstract

We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school environments, they alone are not sufficient to avoid significant impacts on community transmission.
Original languageEnglish
Article number100094
Number of pages11
JournalGlobal Epidemiology
Volume4
Early online date24 Nov 2022
DOIs
Publication statusPublished - Dec 2022

Bibliographical note

This study was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) [grant number 402834/2020-8]. MEB received a technological and industrial scholarship from CNPq [grant number 315854/2020-0]. LSF received a master's scholarship from Coordination for the Improvement of Higher Education Personnel (CAPES) [finance code 001]. SP was supported by São Paulo Research Foundation (FAPESP) [grant number 2018/24037-4]. AMB received a technological and industrial scholarship from CNPq [grant number 402834/2020-8]. CF was supported by FAPESP [grant numbers 2019/26310-2 and 2017/26770-8]. MQMR received a postdoctoral scholarship from CAPES [grant number 305269/2020-8]. LMS received a technological and industrial scholarship from CNPq [grant number 315866/2020-9]. RSK has been supported by CNPq [grant number 312378/2019-0]. PIP has been supported by CNPq [grant number 313055/2020-3]. JAFD-F has been supported by CNPq productivity fellowship and the National Institutes for Science and Technology in Ecology, Evolution and Biodiversity Conservation (INCT-EEC), supported by MCTIC/CNPq [grant number 465610/2014-5] and Goiás Research Foundation (FAPEG) [grant number 201810267000023]. RAK has been supported by CNPq [grant number 311832/2017-2] and FAPESP [grant number 2016/01343-7]. CMT has been supported by CNPq productivity fellowship and the National Institute for Health Technology Assessment (IATS) [grant number 465518/2014-1].

Data Availability Statement

Supplementary data: Supplementary data to this article can be found online at https://doi.
org/10.1016/j.gloepi.2022.100094.

Keywords

  • decision support techniques
  • COVID-19
  • Brazil
  • schools
  • non-pharmaceutical interventions
  • dynamic transmission models

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